Overview

Brought to you by YData

Dataset statistics

Number of variables114
Number of observations60976
Missing cells0
Missing cells (%)0.0%
Duplicate rows1720
Duplicate rows (%)2.8%
Total size in memory53.5 MiB
Average record size in memory920.0 B

Variable types

Numeric16
Categorical98

Alerts

Dataset has 1720 (2.8%) duplicate rowsDuplicates
model_hashed_0 is highly imbalanced (87.3%)Imbalance
model_hashed_1 is highly imbalanced (90.6%)Imbalance
model_hashed_2 is highly imbalanced (87.9%)Imbalance
model_hashed_3 is highly imbalanced (93.5%)Imbalance
model_hashed_4 is highly imbalanced (92.3%)Imbalance
model_hashed_5 is highly imbalanced (93.7%)Imbalance
model_hashed_6 is highly imbalanced (97.0%)Imbalance
model_hashed_7 is highly imbalanced (89.0%)Imbalance
model_hashed_8 is highly imbalanced (91.1%)Imbalance
model_hashed_9 is highly imbalanced (86.0%)Imbalance
model_hashed_10 is highly imbalanced (90.1%)Imbalance
model_hashed_11 is highly imbalanced (88.6%)Imbalance
model_hashed_12 is highly imbalanced (92.1%)Imbalance
model_hashed_13 is highly imbalanced (93.0%)Imbalance
model_hashed_14 is highly imbalanced (91.3%)Imbalance
model_hashed_15 is highly imbalanced (92.9%)Imbalance
model_hashed_16 is highly imbalanced (91.8%)Imbalance
model_hashed_17 is highly imbalanced (90.3%)Imbalance
model_hashed_18 is highly imbalanced (85.6%)Imbalance
model_hashed_19 is highly imbalanced (88.3%)Imbalance
model_hashed_20 is highly imbalanced (96.5%)Imbalance
model_hashed_21 is highly imbalanced (81.3%)Imbalance
model_hashed_22 is highly imbalanced (90.3%)Imbalance
model_hashed_23 is highly imbalanced (84.5%)Imbalance
model_hashed_24 is highly imbalanced (93.0%)Imbalance
model_hashed_25 is highly imbalanced (88.7%)Imbalance
model_hashed_26 is highly imbalanced (78.6%)Imbalance
model_hashed_27 is highly imbalanced (90.5%)Imbalance
model_hashed_28 is highly imbalanced (89.0%)Imbalance
model_hashed_29 is highly imbalanced (81.1%)Imbalance
model_hashed_30 is highly imbalanced (87.9%)Imbalance
model_hashed_31 is highly imbalanced (97.0%)Imbalance
model_hashed_32 is highly imbalanced (96.8%)Imbalance
model_hashed_33 is highly imbalanced (88.0%)Imbalance
model_hashed_34 is highly imbalanced (94.0%)Imbalance
model_hashed_35 is highly imbalanced (78.7%)Imbalance
model_hashed_36 is highly imbalanced (86.7%)Imbalance
model_hashed_37 is highly imbalanced (91.0%)Imbalance
model_hashed_38 is highly imbalanced (87.4%)Imbalance
model_hashed_39 is highly imbalanced (81.2%)Imbalance
model_hashed_40 is highly imbalanced (91.8%)Imbalance
model_hashed_41 is highly imbalanced (93.8%)Imbalance
model_hashed_42 is highly imbalanced (94.1%)Imbalance
model_hashed_43 is highly imbalanced (87.3%)Imbalance
model_hashed_44 is highly imbalanced (94.1%)Imbalance
model_hashed_45 is highly imbalanced (89.8%)Imbalance
model_hashed_46 is highly imbalanced (97.7%)Imbalance
drivetrain_Rear-wheel Drive is highly imbalanced (89.9%)Imbalance
drivetrain_nan is highly imbalanced (97.7%)Imbalance
make_Acura is highly imbalanced (85.6%)Imbalance
make_Alfa Romeo is highly imbalanced (95.8%)Imbalance
make_Audi is highly imbalanced (76.3%)Imbalance
make_BMW is highly imbalanced (75.3%)Imbalance
make_Buick is highly imbalanced (86.5%)Imbalance
make_Cadillac is highly imbalanced (83.5%)Imbalance
make_Chevrolet is highly imbalanced (55.3%)Imbalance
make_Chrysler is highly imbalanced (96.5%)Imbalance
make_Dodge is highly imbalanced (86.5%)Imbalance
make_Ford is highly imbalanced (56.1%)Imbalance
make_GMC is highly imbalanced (81.9%)Imbalance
make_Genesis is highly imbalanced (94.7%)Imbalance
make_Honda is highly imbalanced (72.7%)Imbalance
make_Hyundai is highly imbalanced (68.8%)Imbalance
make_INFINITI is highly imbalanced (89.8%)Imbalance
make_Jaguar is highly imbalanced (96.3%)Imbalance
make_Jeep is highly imbalanced (58.4%)Imbalance
make_Kia is highly imbalanced (77.7%)Imbalance
make_Land Rover is highly imbalanced (89.1%)Imbalance
make_Lexus is highly imbalanced (85.7%)Imbalance
make_Lincoln is highly imbalanced (86.6%)Imbalance
make_Mazda is highly imbalanced (77.3%)Imbalance
make_Mercedes-Benz is highly imbalanced (70.4%)Imbalance
make_Nissan is highly imbalanced (65.1%)Imbalance
make_Porsche is highly imbalanced (95.5%)Imbalance
make_RAM is highly imbalanced (87.9%)Imbalance
make_Subaru is highly imbalanced (75.6%)Imbalance
make_Tesla is highly imbalanced (97.7%)Imbalance
make_Toyota is highly imbalanced (76.8%)Imbalance
make_Volkswagen is highly imbalanced (70.9%)Imbalance
make_Volvo is highly imbalanced (91.3%)Imbalance
bodystyle_Cargo Van is highly imbalanced (96.1%)Imbalance
bodystyle_Convertible is highly imbalanced (95.0%)Imbalance
bodystyle_Coupe is highly imbalanced (93.5%)Imbalance
bodystyle_Hatchback is highly imbalanced (90.4%)Imbalance
bodystyle_Minivan is highly imbalanced (97.8%)Imbalance
bodystyle_Passenger Van is highly imbalanced (96.1%)Imbalance
bodystyle_Pickup Truck is highly imbalanced (57.1%)Imbalance
bodystyle_Wagon is highly imbalanced (97.7%)Imbalance
bodystyle_nan is highly imbalanced (97.7%)Imbalance
fuel_type_Electric is highly imbalanced (80.2%)Imbalance
fuel_type_Flexible is highly imbalanced (97.4%)Imbalance
fuel_type_Gasoline is highly imbalanced (66.4%)Imbalance
fuel_type_Hybrid is highly imbalanced (81.1%)Imbalance
mileage has 3000 (4.9%) zerosZeros
exterior_color_x4 has 1746 (2.9%) zerosZeros
cat_x1 has 2218 (3.6%) zerosZeros

Reproduction

Analysis started2024-07-25 00:16:45.370415
Analysis finished2024-07-25 00:17:23.198641
Duration37.83 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94469508
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:23.313713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.81818182
Q10.92727273
median0.98181818
Q30.98181818
95-th percentile0.98181818
Maximum1
Range1
Interquartile range (IQR)0.054545455

Descriptive statistics

Standard deviation0.061540845
Coefficient of variation (CV)0.065143607
Kurtosis7.0936608
Mean0.94469508
Median Absolute Deviation (MAD)0
Skewness-2.2407077
Sum57603.727
Variance0.0037872756
MonotonicityNot monotonic
2024-07-24T19:17:23.470934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.9818181818 32360
53.1%
0.9272727273 5764
 
9.5%
0.9636363636 4628
 
7.6%
0.9454545455 3057
 
5.0%
0.9090909091 2947
 
4.8%
0.8909090909 2179
 
3.6%
0.8727272727 1823
 
3.0%
1 1747
 
2.9%
0.8545454545 1499
 
2.5%
0.8363636364 1210
 
2.0%
Other values (25) 3762
 
6.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.2545454545 2
 
< 0.1%
0.2727272727 1
 
< 0.1%
0.4 2
 
< 0.1%
0.4181818182 1
 
< 0.1%
0.4727272727 2
 
< 0.1%
0.4909090909 1
 
< 0.1%
0.5090909091 5
< 0.1%
0.5272727273 6
< 0.1%
0.5454545455 8
< 0.1%
ValueCountFrequency (%)
1 1747
 
2.9%
0.9818181818 32360
53.1%
0.9636363636 4628
 
7.6%
0.9454545455 3057
 
5.0%
0.9272727273 5764
 
9.5%
0.9090909091 2947
 
4.8%
0.8909090909 2179
 
3.6%
0.8727272727 1823
 
3.0%
0.8545454545 1499
 
2.5%
0.8363636364 1210
 
2.0%

price
Real number (ℝ)

Distinct24891
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10467389
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:23.620138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.034076081
Q10.06548919
median0.090139769
Q30.13226278
95-th percentile0.2138622
Maximum1
Range1
Interquartile range (IQR)0.066773586

Descriptive statistics

Standard deviation0.060657624
Coefficient of variation (CV)0.57949143
Kurtosis10.508805
Mean0.10467389
Median Absolute Deviation (MAD)0.031120332
Skewness2.1315978
Sum6382.5954
Variance0.0036793474
MonotonicityNot monotonic
2024-07-24T19:17:23.780544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04229908277 101
 
0.2%
0.05594835117 91
 
0.1%
0.05049683337 90
 
0.1%
0.04775879013 88
 
0.1%
0.05048864381 88
 
0.1%
0.04502893645 88
 
0.1%
0.06140805853 85
 
0.1%
0.07778718061 85
 
0.1%
0.03956922909 85
 
0.1%
0.03683937541 83
 
0.1%
Other values (24881) 60092
98.6%
ValueCountFrequency (%)
0 1
< 0.1%
0.001091941472 1
< 0.1%
0.001351277572 1
< 0.1%
0.00136492684 2
< 0.1%
0.002183882944 1
< 0.1%
0.002716204411 1
< 0.1%
0.00272985368 1
< 0.1%
0.003262175147 1
< 0.1%
0.003821795152 2
< 0.1%
0.004067481983 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9780656257 1
< 0.1%
0.9414036908 1
< 0.1%
0.816772221 1
< 0.1%
0.7597182791 1
< 0.1%
0.7349039092 1
< 0.1%
0.7257452501 1
< 0.1%
0.6897248307 1
< 0.1%
0.6815352697 1
< 0.1%
0.6765123389 1
< 0.1%

mileage
Real number (ℝ)

ZEROS 

Distinct25776
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050994288
Minimum0
Maximum1
Zeros3000
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:23.935543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.2680314 × 10-6
Q11.1340157 × 10-5
median6.5772911 × 10-5
Q30.080620579
95-th percentile0.22913411
Maximum1
Range1
Interquartile range (IQR)0.080609239

Descriptive statistics

Standard deviation0.082239747
Coefficient of variation (CV)1.6127247
Kurtosis4.5486656
Mean0.050994288
Median Absolute Deviation (MAD)6.5772911 × 10-5
Skewness1.9845025
Sum3109.4277
Variance0.006763376
MonotonicityNot monotonic
2024-07-24T19:17:24.102637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.134015708 × 10-54928
 
8.1%
2.268031417 × 10-54006
 
6.6%
0 3000
 
4.9%
6.80409425 × 10-62513
 
4.1%
1.36081885 × 10-52235
 
3.7%
4.536062834 × 10-61966
 
3.2%
2.268031417 × 10-61650
 
2.7%
1.587621992 × 10-51472
 
2.4%
9.072125667 × 10-61423
 
2.3%
2.494834558 × 10-51387
 
2.3%
Other values (25766) 36396
59.7%
ValueCountFrequency (%)
0 3000
4.9%
2.268031417 × 10-61650
2.7%
4.413211132 × 10-61
 
< 0.1%
4.536062834 × 10-61966
3.2%
4.785546289 × 10-61
 
< 0.1%
6.237086396 × 10-61
 
< 0.1%
6.395848595 × 10-61
 
< 0.1%
6.463889538 × 10-61
 
< 0.1%
6.80409425 × 10-62513
4.1%
7.461823361 × 10-61
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.8736978665 1
< 0.1%
0.735633722 1
< 0.1%
0.720254201 1
< 0.1%
0.7030897392 2
< 0.1%
0.6904454641 1
< 0.1%
0.6758733622 1
< 0.1%
0.6418528909 1
< 0.1%
0.6358902364 1
< 0.1%
0.6134979622 1
< 0.1%

stock_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
1.0
34254 
0.0
26722 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 34254
56.2%
0.0 26722
43.8%

Length

2024-07-24T19:17:24.246117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:24.377125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 34254
56.2%
0.0 26722
43.8%

Most occurring characters

ValueCountFrequency (%)
0 87698
47.9%
. 60976
33.3%
1 34254
 
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 87698
47.9%
. 60976
33.3%
1 34254
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 87698
47.9%
. 60976
33.3%
1 34254
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 87698
47.9%
. 60976
33.3%
1 34254
 
18.7%

model_hashed_0
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59159 
0.0
 
1733
1.0
 
84

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59159
97.0%
0.0 1733
 
2.8%
1.0 84
 
0.1%

Length

2024-07-24T19:17:24.695813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:24.808866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59159
97.0%
0.0 1733
 
2.8%
1.0 84
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 62709
34.3%
. 60976
33.3%
5 59159
32.3%
1 84
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62709
34.3%
. 60976
33.3%
5 59159
32.3%
1 84
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62709
34.3%
. 60976
33.3%
5 59159
32.3%
1 84
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62709
34.3%
. 60976
33.3%
5 59159
32.3%
1 84
 
< 0.1%

model_hashed_1
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59853 
1.0
 
792
0.0
 
331

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59853
98.2%
1.0 792
 
1.3%
0.0 331
 
0.5%

Length

2024-07-24T19:17:24.931733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:25.046900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59853
98.2%
1.0 792
 
1.3%
0.0 331
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 61307
33.5%
. 60976
33.3%
5 59853
32.7%
1 792
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61307
33.5%
. 60976
33.3%
5 59853
32.7%
1 792
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61307
33.5%
. 60976
33.3%
5 59853
32.7%
1 792
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61307
33.5%
. 60976
33.3%
5 59853
32.7%
1 792
 
0.4%

model_hashed_2
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59383 
1.0
 
1302
0.0
 
291

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59383
97.4%
1.0 1302
 
2.1%
0.0 291
 
0.5%

Length

2024-07-24T19:17:25.173755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:25.289956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59383
97.4%
1.0 1302
 
2.1%
0.0 291
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 61267
33.5%
. 60976
33.3%
5 59383
32.5%
1 1302
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61267
33.5%
. 60976
33.3%
5 59383
32.5%
1 1302
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61267
33.5%
. 60976
33.3%
5 59383
32.5%
1 1302
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61267
33.5%
. 60976
33.3%
5 59383
32.5%
1 1302
 
0.7%

model_hashed_3
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60261 
0.0
 
467
1.0
 
248

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60261
98.8%
0.0 467
 
0.8%
1.0 248
 
0.4%

Length

2024-07-24T19:17:25.414147image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:25.528087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60261
98.8%
0.0 467
 
0.8%
1.0 248
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 61443
33.6%
. 60976
33.3%
5 60261
32.9%
1 248
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61443
33.6%
. 60976
33.3%
5 60261
32.9%
1 248
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61443
33.6%
. 60976
33.3%
5 60261
32.9%
1 248
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61443
33.6%
. 60976
33.3%
5 60261
32.9%
1 248
 
0.1%

model_hashed_4
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60103 
1.0
 
490
0.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60103
98.6%
1.0 490
 
0.8%
0.0 383
 
0.6%

Length

2024-07-24T19:17:25.648635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:25.766181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60103
98.6%
1.0 490
 
0.8%
0.0 383
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 61359
33.5%
. 60976
33.3%
5 60103
32.9%
1 490
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61359
33.5%
. 60976
33.3%
5 60103
32.9%
1 490
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61359
33.5%
. 60976
33.3%
5 60103
32.9%
1 490
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61359
33.5%
. 60976
33.3%
5 60103
32.9%
1 490
 
0.3%

model_hashed_5
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60240 
1.0
 
654
0.0
 
82

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60240
98.8%
1.0 654
 
1.1%
0.0 82
 
0.1%

Length

2024-07-24T19:17:25.887007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:26.004584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60240
98.8%
1.0 654
 
1.1%
0.0 82
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 61058
33.4%
. 60976
33.3%
5 60240
32.9%
1 654
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61058
33.4%
. 60976
33.3%
5 60240
32.9%
1 654
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61058
33.4%
. 60976
33.3%
5 60240
32.9%
1 654
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61058
33.4%
. 60976
33.3%
5 60240
32.9%
1 654
 
0.4%

model_hashed_6
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60668 
1.0
 
297
0.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60668
99.5%
1.0 297
 
0.5%
0.0 11
 
< 0.1%

Length

2024-07-24T19:17:26.130852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:26.249597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60668
99.5%
1.0 297
 
0.5%
0.0 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 60987
33.3%
. 60976
33.3%
5 60668
33.2%
1 297
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 60987
33.3%
. 60976
33.3%
5 60668
33.2%
1 297
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 60987
33.3%
. 60976
33.3%
5 60668
33.2%
1 297
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 60987
33.3%
. 60976
33.3%
5 60668
33.2%
1 297
 
0.2%

model_hashed_7
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59631 
0.0
 
856
1.0
 
489

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59631
97.8%
0.0 856
 
1.4%
1.0 489
 
0.8%

Length

2024-07-24T19:17:26.373460image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:26.479061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59631
97.8%
0.0 856
 
1.4%
1.0 489
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 61832
33.8%
. 60976
33.3%
5 59631
32.6%
1 489
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61832
33.8%
. 60976
33.3%
5 59631
32.6%
1 489
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61832
33.8%
. 60976
33.3%
5 59631
32.6%
1 489
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61832
33.8%
. 60976
33.3%
5 59631
32.6%
1 489
 
0.3%

model_hashed_8
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59932 
1.0
 
695
0.0
 
349

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59932
98.3%
1.0 695
 
1.1%
0.0 349
 
0.6%

Length

2024-07-24T19:17:26.600055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:26.712607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59932
98.3%
1.0 695
 
1.1%
0.0 349
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 61325
33.5%
. 60976
33.3%
5 59932
32.8%
1 695
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61325
33.5%
. 60976
33.3%
5 59932
32.8%
1 695
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61325
33.5%
. 60976
33.3%
5 59932
32.8%
1 695
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61325
33.5%
. 60976
33.3%
5 59932
32.8%
1 695
 
0.4%

model_hashed_9
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59151 
0.0
 
1195
1.0
 
630

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59151
97.0%
0.0 1195
 
2.0%
1.0 630
 
1.0%

Length

2024-07-24T19:17:26.825789image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:26.926634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59151
97.0%
0.0 1195
 
2.0%
1.0 630
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 62171
34.0%
. 60976
33.3%
5 59151
32.3%
1 630
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62171
34.0%
. 60976
33.3%
5 59151
32.3%
1 630
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62171
34.0%
. 60976
33.3%
5 59151
32.3%
1 630
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62171
34.0%
. 60976
33.3%
5 59151
32.3%
1 630
 
0.3%

model_hashed_10
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59699 
0.0
 
1144
1.0
 
133

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59699
97.9%
0.0 1144
 
1.9%
1.0 133
 
0.2%

Length

2024-07-24T19:17:27.051867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:27.162140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59699
97.9%
0.0 1144
 
1.9%
1.0 133
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 62120
34.0%
. 60976
33.3%
5 59699
32.6%
1 133
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62120
34.0%
. 60976
33.3%
5 59699
32.6%
1 133
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62120
34.0%
. 60976
33.3%
5 59699
32.6%
1 133
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62120
34.0%
. 60976
33.3%
5 59699
32.6%
1 133
 
0.1%

model_hashed_11
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59551 
1.0
 
971
0.0
 
454

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59551
97.7%
1.0 971
 
1.6%
0.0 454
 
0.7%

Length

2024-07-24T19:17:27.275061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:27.385574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59551
97.7%
1.0 971
 
1.6%
0.0 454
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 61430
33.6%
. 60976
33.3%
5 59551
32.6%
1 971
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61430
33.6%
. 60976
33.3%
5 59551
32.6%
1 971
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61430
33.6%
. 60976
33.3%
5 59551
32.6%
1 971
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61430
33.6%
. 60976
33.3%
5 59551
32.6%
1 971
 
0.5%

model_hashed_12
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60066 
1.0
 
606
0.0
 
304

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60066
98.5%
1.0 606
 
1.0%
0.0 304
 
0.5%

Length

2024-07-24T19:17:27.490909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:27.590752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60066
98.5%
1.0 606
 
1.0%
0.0 304
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 61280
33.5%
. 60976
33.3%
5 60066
32.8%
1 606
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61280
33.5%
. 60976
33.3%
5 60066
32.8%
1 606
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61280
33.5%
. 60976
33.3%
5 60066
32.8%
1 606
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61280
33.5%
. 60976
33.3%
5 60066
32.8%
1 606
 
0.3%

model_hashed_13
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60176 
0.0
 
620
1.0
 
180

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60176
98.7%
0.0 620
 
1.0%
1.0 180
 
0.3%

Length

2024-07-24T19:17:27.706534image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:27.813616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60176
98.7%
0.0 620
 
1.0%
1.0 180
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 61596
33.7%
. 60976
33.3%
5 60176
32.9%
1 180
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61596
33.7%
. 60976
33.3%
5 60176
32.9%
1 180
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61596
33.7%
. 60976
33.3%
5 60176
32.9%
1 180
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61596
33.7%
. 60976
33.3%
5 60176
32.9%
1 180
 
0.1%

model_hashed_14
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59846 
1.0
 
1076
0.0
 
54

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59846
98.1%
1.0 1076
 
1.8%
0.0 54
 
0.1%

Length

2024-07-24T19:17:27.924776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:28.037889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59846
98.1%
1.0 1076
 
1.8%
0.0 54
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 61030
33.4%
. 60976
33.3%
5 59846
32.7%
1 1076
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61030
33.4%
. 60976
33.3%
5 59846
32.7%
1 1076
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61030
33.4%
. 60976
33.3%
5 59846
32.7%
1 1076
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61030
33.4%
. 60976
33.3%
5 59846
32.7%
1 1076
 
0.6%

model_hashed_15
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60186 
0.0
 
444
1.0
 
346

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60186
98.7%
0.0 444
 
0.7%
1.0 346
 
0.6%

Length

2024-07-24T19:17:28.156777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:28.269103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60186
98.7%
0.0 444
 
0.7%
1.0 346
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 61420
33.6%
. 60976
33.3%
5 60186
32.9%
1 346
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61420
33.6%
. 60976
33.3%
5 60186
32.9%
1 346
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61420
33.6%
. 60976
33.3%
5 60186
32.9%
1 346
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61420
33.6%
. 60976
33.3%
5 60186
32.9%
1 346
 
0.2%

model_hashed_16
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59934 
1.0
 
990
0.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59934
98.3%
1.0 990
 
1.6%
0.0 52
 
0.1%

Length

2024-07-24T19:17:28.390600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:28.504900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59934
98.3%
1.0 990
 
1.6%
0.0 52
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 61028
33.4%
. 60976
33.3%
5 59934
32.8%
1 990
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61028
33.4%
. 60976
33.3%
5 59934
32.8%
1 990
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61028
33.4%
. 60976
33.3%
5 59934
32.8%
1 990
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61028
33.4%
. 60976
33.3%
5 59934
32.8%
1 990
 
0.5%

model_hashed_17
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59797 
1.0
 
842
0.0
 
337

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row1.0
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59797
98.1%
1.0 842
 
1.4%
0.0 337
 
0.6%

Length

2024-07-24T19:17:28.621067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:28.726196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59797
98.1%
1.0 842
 
1.4%
0.0 337
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 61313
33.5%
. 60976
33.3%
5 59797
32.7%
1 842
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61313
33.5%
. 60976
33.3%
5 59797
32.7%
1 842
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61313
33.5%
. 60976
33.3%
5 59797
32.7%
1 842
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61313
33.5%
. 60976
33.3%
5 59797
32.7%
1 842
 
0.5%

model_hashed_18
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
58967 
0.0
 
1729
1.0
 
280

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 58967
96.7%
0.0 1729
 
2.8%
1.0 280
 
0.5%

Length

2024-07-24T19:17:28.834647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:28.932784image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 58967
96.7%
0.0 1729
 
2.8%
1.0 280
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 62705
34.3%
. 60976
33.3%
5 58967
32.2%
1 280
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62705
34.3%
. 60976
33.3%
5 58967
32.2%
1 280
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62705
34.3%
. 60976
33.3%
5 58967
32.2%
1 280
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62705
34.3%
. 60976
33.3%
5 58967
32.2%
1 280
 
0.2%

model_hashed_19
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59509 
1.0
 
997
0.0
 
470

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59509
97.6%
1.0 997
 
1.6%
0.0 470
 
0.8%

Length

2024-07-24T19:17:29.043156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:29.146493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59509
97.6%
1.0 997
 
1.6%
0.0 470
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 61446
33.6%
. 60976
33.3%
5 59509
32.5%
1 997
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61446
33.6%
. 60976
33.3%
5 59509
32.5%
1 997
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61446
33.6%
. 60976
33.3%
5 59509
32.5%
1 997
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61446
33.6%
. 60976
33.3%
5 59509
32.5%
1 997
 
0.5%

model_hashed_20
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60628 
0.0
 
222
1.0
 
126

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60628
99.4%
0.0 222
 
0.4%
1.0 126
 
0.2%

Length

2024-07-24T19:17:29.254878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:29.358402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60628
99.4%
0.0 222
 
0.4%
1.0 126
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 61198
33.5%
. 60976
33.3%
5 60628
33.1%
1 126
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61198
33.5%
. 60976
33.3%
5 60628
33.1%
1 126
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61198
33.5%
. 60976
33.3%
5 60628
33.1%
1 126
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61198
33.5%
. 60976
33.3%
5 60628
33.1%
1 126
 
0.1%

model_hashed_21
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
58224 
1.0
 
2226
0.0
 
526

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.0

Common Values

ValueCountFrequency (%)
0.5 58224
95.5%
1.0 2226
 
3.7%
0.0 526
 
0.9%

Length

2024-07-24T19:17:29.466287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:29.579756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 58224
95.5%
1.0 2226
 
3.7%
0.0 526
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 61502
33.6%
. 60976
33.3%
5 58224
31.8%
1 2226
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61502
33.6%
. 60976
33.3%
5 58224
31.8%
1 2226
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61502
33.6%
. 60976
33.3%
5 58224
31.8%
1 2226
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61502
33.6%
. 60976
33.3%
5 58224
31.8%
1 2226
 
1.2%

model_hashed_22
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59746 
1.0
 
1062
0.0
 
168

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59746
98.0%
1.0 1062
 
1.7%
0.0 168
 
0.3%

Length

2024-07-24T19:17:29.690327image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:29.802477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59746
98.0%
1.0 1062
 
1.7%
0.0 168
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 61144
33.4%
. 60976
33.3%
5 59746
32.7%
1 1062
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61144
33.4%
. 60976
33.3%
5 59746
32.7%
1 1062
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61144
33.4%
. 60976
33.3%
5 59746
32.7%
1 1062
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61144
33.4%
. 60976
33.3%
5 59746
32.7%
1 1062
 
0.6%

model_hashed_23
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
58879 
1.0
 
1524
0.0
 
573

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 58879
96.6%
1.0 1524
 
2.5%
0.0 573
 
0.9%

Length

2024-07-24T19:17:29.915246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:30.014539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 58879
96.6%
1.0 1524
 
2.5%
0.0 573
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 61549
33.6%
. 60976
33.3%
5 58879
32.2%
1 1524
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61549
33.6%
. 60976
33.3%
5 58879
32.2%
1 1524
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61549
33.6%
. 60976
33.3%
5 58879
32.2%
1 1524
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61549
33.6%
. 60976
33.3%
5 58879
32.2%
1 1524
 
0.8%

model_hashed_24
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60200 
1.0
 
405
0.0
 
371

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60200
98.7%
1.0 405
 
0.7%
0.0 371
 
0.6%

Length

2024-07-24T19:17:30.134642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:30.240187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60200
98.7%
1.0 405
 
0.7%
0.0 371
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 61347
33.5%
. 60976
33.3%
5 60200
32.9%
1 405
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61347
33.5%
. 60976
33.3%
5 60200
32.9%
1 405
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61347
33.5%
. 60976
33.3%
5 60200
32.9%
1 405
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61347
33.5%
. 60976
33.3%
5 60200
32.9%
1 405
 
0.2%

model_hashed_25
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59586 
0.0
 
727
1.0
 
663

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59586
97.7%
0.0 727
 
1.2%
1.0 663
 
1.1%

Length

2024-07-24T19:17:30.358050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:30.623166image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59586
97.7%
0.0 727
 
1.2%
1.0 663
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 61703
33.7%
. 60976
33.3%
5 59586
32.6%
1 663
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61703
33.7%
. 60976
33.3%
5 59586
32.6%
1 663
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61703
33.7%
. 60976
33.3%
5 59586
32.6%
1 663
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61703
33.7%
. 60976
33.3%
5 59586
32.6%
1 663
 
0.4%

model_hashed_26
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
57887 
0.0
 
1619
1.0
 
1470

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 57887
94.9%
0.0 1619
 
2.7%
1.0 1470
 
2.4%

Length

2024-07-24T19:17:30.733714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:30.836632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 57887
94.9%
0.0 1619
 
2.7%
1.0 1470
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 62595
34.2%
. 60976
33.3%
5 57887
31.6%
1 1470
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62595
34.2%
. 60976
33.3%
5 57887
31.6%
1 1470
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62595
34.2%
. 60976
33.3%
5 57887
31.6%
1 1470
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62595
34.2%
. 60976
33.3%
5 57887
31.6%
1 1470
 
0.8%

model_hashed_27
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59840 
0.0
 
785
1.0
 
351

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59840
98.1%
0.0 785
 
1.3%
1.0 351
 
0.6%

Length

2024-07-24T19:17:30.946180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:31.049573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59840
98.1%
0.0 785
 
1.3%
1.0 351
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 61761
33.8%
. 60976
33.3%
5 59840
32.7%
1 351
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61761
33.8%
. 60976
33.3%
5 59840
32.7%
1 351
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61761
33.8%
. 60976
33.3%
5 59840
32.7%
1 351
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61761
33.8%
. 60976
33.3%
5 59840
32.7%
1 351
 
0.2%

model_hashed_28
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59639 
1.0
 
769
0.0
 
568

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59639
97.8%
1.0 769
 
1.3%
0.0 568
 
0.9%

Length

2024-07-24T19:17:31.160588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:31.261549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59639
97.8%
1.0 769
 
1.3%
0.0 568
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 61544
33.6%
. 60976
33.3%
5 59639
32.6%
1 769
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61544
33.6%
. 60976
33.3%
5 59639
32.6%
1 769
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61544
33.6%
. 60976
33.3%
5 59639
32.6%
1 769
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61544
33.6%
. 60976
33.3%
5 59639
32.6%
1 769
 
0.4%

model_hashed_29
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
58287 
0.0
 
1878
1.0
 
811

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 58287
95.6%
0.0 1878
 
3.1%
1.0 811
 
1.3%

Length

2024-07-24T19:17:31.373672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:31.474136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 58287
95.6%
0.0 1878
 
3.1%
1.0 811
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 62854
34.4%
. 60976
33.3%
5 58287
31.9%
1 811
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62854
34.4%
. 60976
33.3%
5 58287
31.9%
1 811
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62854
34.4%
. 60976
33.3%
5 58287
31.9%
1 811
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62854
34.4%
. 60976
33.3%
5 58287
31.9%
1 811
 
0.4%

model_hashed_30
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59469 
0.0
 
829
1.0
 
678

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59469
97.5%
0.0 829
 
1.4%
1.0 678
 
1.1%

Length

2024-07-24T19:17:31.582703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:31.678613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59469
97.5%
0.0 829
 
1.4%
1.0 678
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 61805
33.8%
. 60976
33.3%
5 59469
32.5%
1 678
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61805
33.8%
. 60976
33.3%
5 59469
32.5%
1 678
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61805
33.8%
. 60976
33.3%
5 59469
32.5%
1 678
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61805
33.8%
. 60976
33.3%
5 59469
32.5%
1 678
 
0.4%

model_hashed_31
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60673 
1.0
 
274
0.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60673
99.5%
1.0 274
 
0.4%
0.0 29
 
< 0.1%

Length

2024-07-24T19:17:31.786493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:31.886914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60673
99.5%
1.0 274
 
0.4%
0.0 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 61005
33.3%
. 60976
33.3%
5 60673
33.2%
1 274
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61005
33.3%
. 60976
33.3%
5 60673
33.2%
1 274
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61005
33.3%
. 60976
33.3%
5 60673
33.2%
1 274
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61005
33.3%
. 60976
33.3%
5 60673
33.2%
1 274
 
0.1%

model_hashed_32
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60664 
1.0
 
240
0.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60664
99.5%
1.0 240
 
0.4%
0.0 72
 
0.1%

Length

2024-07-24T19:17:31.994024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:32.092680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60664
99.5%
1.0 240
 
0.4%
0.0 72
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 61048
33.4%
. 60976
33.3%
5 60664
33.2%
1 240
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61048
33.4%
. 60976
33.3%
5 60664
33.2%
1 240
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61048
33.4%
. 60976
33.3%
5 60664
33.2%
1 240
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61048
33.4%
. 60976
33.3%
5 60664
33.2%
1 240
 
0.1%

model_hashed_33
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59389 
0.0
 
1349
1.0
 
238

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59389
97.4%
0.0 1349
 
2.2%
1.0 238
 
0.4%

Length

2024-07-24T19:17:32.199687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:32.313002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59389
97.4%
0.0 1349
 
2.2%
1.0 238
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 62325
34.1%
. 60976
33.3%
5 59389
32.5%
1 238
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62325
34.1%
. 60976
33.3%
5 59389
32.5%
1 238
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62325
34.1%
. 60976
33.3%
5 59389
32.5%
1 238
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62325
34.1%
. 60976
33.3%
5 59389
32.5%
1 238
 
0.1%

model_hashed_34
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60300 
0.0
 
569
1.0
 
107

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.0
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60300
98.9%
0.0 569
 
0.9%
1.0 107
 
0.2%

Length

2024-07-24T19:17:32.438851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:32.561468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60300
98.9%
0.0 569
 
0.9%
1.0 107
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 61545
33.6%
. 60976
33.3%
5 60300
33.0%
1 107
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61545
33.6%
. 60976
33.3%
5 60300
33.0%
1 107
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61545
33.6%
. 60976
33.3%
5 60300
33.0%
1 107
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61545
33.6%
. 60976
33.3%
5 60300
33.0%
1 107
 
0.1%

model_hashed_35
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
57845 
1.0
 
2169
0.0
 
962

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 57845
94.9%
1.0 2169
 
3.6%
0.0 962
 
1.6%

Length

2024-07-24T19:17:32.685277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:32.799731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 57845
94.9%
1.0 2169
 
3.6%
0.0 962
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 61938
33.9%
. 60976
33.3%
5 57845
31.6%
1 2169
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61938
33.9%
. 60976
33.3%
5 57845
31.6%
1 2169
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61938
33.9%
. 60976
33.3%
5 57845
31.6%
1 2169
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61938
33.9%
. 60976
33.3%
5 57845
31.6%
1 2169
 
1.2%

model_hashed_36
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59233 
1.0
 
1320
0.0
 
423

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row1.0
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59233
97.1%
1.0 1320
 
2.2%
0.0 423
 
0.7%

Length

2024-07-24T19:17:32.922023image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:33.032747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59233
97.1%
1.0 1320
 
2.2%
0.0 423
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 61399
33.6%
. 60976
33.3%
5 59233
32.4%
1 1320
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61399
33.6%
. 60976
33.3%
5 59233
32.4%
1 1320
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61399
33.6%
. 60976
33.3%
5 59233
32.4%
1 1320
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61399
33.6%
. 60976
33.3%
5 59233
32.4%
1 1320
 
0.7%

model_hashed_37
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59862 
0.0
 
954
1.0
 
160

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59862
98.2%
0.0 954
 
1.6%
1.0 160
 
0.3%

Length

2024-07-24T19:17:33.162197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:33.270036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59862
98.2%
0.0 954
 
1.6%
1.0 160
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 61930
33.9%
. 60976
33.3%
5 59862
32.7%
1 160
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61930
33.9%
. 60976
33.3%
5 59862
32.7%
1 160
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61930
33.9%
. 60976
33.3%
5 59862
32.7%
1 160
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61930
33.9%
. 60976
33.3%
5 59862
32.7%
1 160
 
0.1%

model_hashed_38
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59368 
0.0
 
1119
1.0
 
489

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59368
97.4%
0.0 1119
 
1.8%
1.0 489
 
0.8%

Length

2024-07-24T19:17:33.394066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:33.492677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59368
97.4%
0.0 1119
 
1.8%
1.0 489
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 62095
33.9%
. 60976
33.3%
5 59368
32.5%
1 489
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62095
33.9%
. 60976
33.3%
5 59368
32.5%
1 489
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62095
33.9%
. 60976
33.3%
5 59368
32.5%
1 489
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62095
33.9%
. 60976
33.3%
5 59368
32.5%
1 489
 
0.3%

model_hashed_39
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
58365 
1.0
 
1414
0.0
 
1197

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 58365
95.7%
1.0 1414
 
2.3%
0.0 1197
 
2.0%

Length

2024-07-24T19:17:33.604249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:33.705747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 58365
95.7%
1.0 1414
 
2.3%
0.0 1197
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 62173
34.0%
. 60976
33.3%
5 58365
31.9%
1 1414
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62173
34.0%
. 60976
33.3%
5 58365
31.9%
1 1414
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62173
34.0%
. 60976
33.3%
5 58365
31.9%
1 1414
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62173
34.0%
. 60976
33.3%
5 58365
31.9%
1 1414
 
0.8%

model_hashed_40
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60016 
0.0
 
700
1.0
 
260

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60016
98.4%
0.0 700
 
1.1%
1.0 260
 
0.4%

Length

2024-07-24T19:17:33.838896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:33.965935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60016
98.4%
0.0 700
 
1.1%
1.0 260
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 61676
33.7%
. 60976
33.3%
5 60016
32.8%
1 260
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61676
33.7%
. 60976
33.3%
5 60016
32.8%
1 260
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61676
33.7%
. 60976
33.3%
5 60016
32.8%
1 260
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61676
33.7%
. 60976
33.3%
5 60016
32.8%
1 260
 
0.1%

model_hashed_41
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60252 
1.0
 
654
0.0
 
70

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60252
98.8%
1.0 654
 
1.1%
0.0 70
 
0.1%

Length

2024-07-24T19:17:34.091185image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:34.198686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60252
98.8%
1.0 654
 
1.1%
0.0 70
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 61046
33.4%
. 60976
33.3%
5 60252
32.9%
1 654
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61046
33.4%
. 60976
33.3%
5 60252
32.9%
1 654
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61046
33.4%
. 60976
33.3%
5 60252
32.9%
1 654
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61046
33.4%
. 60976
33.3%
5 60252
32.9%
1 654
 
0.4%

model_hashed_42
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60338 
1.0
 
337
0.0
 
301

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60338
99.0%
1.0 337
 
0.6%
0.0 301
 
0.5%

Length

2024-07-24T19:17:34.326306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:34.443164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60338
99.0%
1.0 337
 
0.6%
0.0 301
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 61277
33.5%
. 60976
33.3%
5 60338
33.0%
1 337
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61277
33.5%
. 60976
33.3%
5 60338
33.0%
1 337
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61277
33.5%
. 60976
33.3%
5 60338
33.0%
1 337
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61277
33.5%
. 60976
33.3%
5 60338
33.0%
1 337
 
0.2%

model_hashed_43
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59370 
0.0
 
994
1.0
 
612

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59370
97.4%
0.0 994
 
1.6%
1.0 612
 
1.0%

Length

2024-07-24T19:17:34.571587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:34.691464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59370
97.4%
0.0 994
 
1.6%
1.0 612
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 61970
33.9%
. 60976
33.3%
5 59370
32.5%
1 612
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61970
33.9%
. 60976
33.3%
5 59370
32.5%
1 612
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61970
33.9%
. 60976
33.3%
5 59370
32.5%
1 612
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61970
33.9%
. 60976
33.3%
5 59370
32.5%
1 612
 
0.3%

model_hashed_44
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60339 
1.0
 
358
0.0
 
279

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60339
99.0%
1.0 358
 
0.6%
0.0 279
 
0.5%

Length

2024-07-24T19:17:34.814244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:34.925395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60339
99.0%
1.0 358
 
0.6%
0.0 279
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 61255
33.5%
. 60976
33.3%
5 60339
33.0%
1 358
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61255
33.5%
. 60976
33.3%
5 60339
33.0%
1 358
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61255
33.5%
. 60976
33.3%
5 60339
33.0%
1 358
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61255
33.5%
. 60976
33.3%
5 60339
33.0%
1 358
 
0.2%

model_hashed_45
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
59617 
1.0
 
1265
0.0
 
94

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 59617
97.8%
1.0 1265
 
2.1%
0.0 94
 
0.2%

Length

2024-07-24T19:17:35.052260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:35.168930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59617
97.8%
1.0 1265
 
2.1%
0.0 94
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 61070
33.4%
. 60976
33.3%
5 59617
32.6%
1 1265
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61070
33.4%
. 60976
33.3%
5 59617
32.6%
1 1265
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61070
33.4%
. 60976
33.3%
5 59617
32.6%
1 1265
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61070
33.4%
. 60976
33.3%
5 59617
32.6%
1 1265
 
0.7%

model_hashed_46
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.5
60768 
0.0
 
127
1.0
 
81

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 60768
99.7%
0.0 127
 
0.2%
1.0 81
 
0.1%

Length

2024-07-24T19:17:35.293955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:35.404627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.5 60768
99.7%
0.0 127
 
0.2%
1.0 81
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 61103
33.4%
. 60976
33.3%
5 60768
33.2%
1 81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61103
33.4%
. 60976
33.3%
5 60768
33.2%
1 81
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61103
33.4%
. 60976
33.3%
5 60768
33.2%
1 81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61103
33.4%
. 60976
33.3%
5 60768
33.2%
1 81
 
< 0.1%

exterior_color_x0
Real number (ℝ)

Distinct2393
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59698382
Minimum0
Maximum1
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:35.548573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30431828
Q10.55504991
median0.64604881
Q30.69134624
95-th percentile0.76910692
Maximum1
Range1
Interquartile range (IQR)0.13629634

Descriptive statistics

Standard deviation0.14910776
Coefficient of variation (CV)0.24976852
Kurtosis1.504022
Mean0.59698382
Median Absolute Deviation (MAD)0.069260343
Skewness-1.2412715
Sum36401.685
Variance0.022233125
MonotonicityNot monotonic
2024-07-24T19:17:35.715256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6460488132 3562
 
5.8%
0.7155001894 1746
 
2.9%
0.5550499079 1617
 
2.7%
0.72225313 1484
 
2.4%
0.316611002 1277
 
2.1%
0.6907022413 765
 
1.3%
0.7307533598 743
 
1.2%
0.594691221 721
 
1.2%
0.6457689591 705
 
1.2%
0.6414813243 681
 
1.1%
Other values (2383) 47675
78.2%
ValueCountFrequency (%)
0 12
 
< 0.1%
0.01878305115 1
 
< 0.1%
0.03756610231 656
1.1%
0.0672087288 2
 
< 0.1%
0.1352752921 3
 
< 0.1%
0.1487582184 119
 
0.2%
0.1535868578 54
 
0.1%
0.1591222998 7
 
< 0.1%
0.1848574628 7
 
< 0.1%
0.1902227171 2
 
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
0.9173006368 6
 
< 0.1%
0.9032562571 7
 
< 0.1%
0.8953984215 27
 
< 0.1%
0.8745527917 5
 
< 0.1%
0.8744731466 135
0.2%
0.8653766826 1
 
< 0.1%
0.8611265663 1
 
< 0.1%
0.8604042264 1
 
< 0.1%
0.8587108206 77
0.1%

exterior_color_x1
Real number (ℝ)

Distinct2390
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31432133
Minimum0
Maximum1
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:35.885648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14316581
Q10.20584699
median0.30742173
Q30.39837901
95-th percentile0.52836511
Maximum1
Range1
Interquartile range (IQR)0.19253201

Descriptive statistics

Standard deviation0.1229824
Coefficient of variation (CV)0.39126331
Kurtosis-0.016843613
Mean0.31432133
Median Absolute Deviation (MAD)0.094679943
Skewness0.51440515
Sum19166.058
Variance0.015124672
MonotonicityNot monotonic
2024-07-24T19:17:36.203787image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1603831853 3562
 
5.8%
0.2058469908 1746
 
2.9%
0.1409525196 1617
 
2.7%
0.3920701592 1484
 
2.4%
0.2909926827 1277
 
2.1%
0.2367261318 765
 
1.3%
0.2865005694 743
 
1.2%
0.2208015957 721
 
1.2%
0.3627862529 705
 
1.2%
0.3505268976 681
 
1.1%
Other values (2380) 47675
78.2%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.07413510684 83
0.1%
0.07664566014 2
 
< 0.1%
0.08681218607 98
0.2%
0.09198471389 102
0.2%
0.09884680571 2
 
< 0.1%
0.1016161106 2
 
< 0.1%
0.1018410404 40
 
0.1%
0.1029234993 4
 
< 0.1%
0.1058123144 135
0.2%
ValueCountFrequency (%)
1 6
 
< 0.1%
0.7243119056 341
0.6%
0.7153712794 2
 
< 0.1%
0.694783553 11
 
< 0.1%
0.674965041 4
 
< 0.1%
0.6701596578 7
 
< 0.1%
0.6623862185 1
 
< 0.1%
0.658549179 66
 
0.1%
0.6547952326 1
 
< 0.1%
0.6509573757 1
 
< 0.1%

exterior_color_x2
Real number (ℝ)

Distinct2394
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41584491
Minimum0
Maximum1
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:36.372012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15197826
Q10.33663431
median0.38894781
Q30.49749052
95-th percentile0.6462761
Maximum1
Range1
Interquartile range (IQR)0.16085621

Descriptive statistics

Standard deviation0.14730006
Coefficient of variation (CV)0.35421875
Kurtosis-0.21054074
Mean0.41584491
Median Absolute Deviation (MAD)0.091003465
Skewness0.10129894
Sum25356.559
Variance0.021697309
MonotonicityNot monotonic
2024-07-24T19:17:36.537695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6462760999 3562
 
5.8%
0.3399698775 1746
 
2.9%
0.3800837458 1617
 
2.7%
0.4841161016 1484
 
2.4%
0.1929344902 1277
 
2.1%
0.1519782609 765
 
1.3%
0.4859598992 743
 
1.2%
0.6207154346 721
 
1.2%
0.4932853838 705
 
1.2%
0.494576516 681
 
1.1%
Other values (2384) 47675
78.2%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.008143006791 2
 
< 0.1%
0.008876380845 4
 
< 0.1%
0.02846343589 8
 
< 0.1%
0.03763178185 6
 
< 0.1%
0.04606726444 187
0.3%
0.05479374754 55
 
0.1%
0.05713972459 12
 
< 0.1%
0.05947935365 16
 
< 0.1%
0.06566595156 23
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9896373519 2
 
< 0.1%
0.8404155137 3
 
< 0.1%
0.8356895996 135
0.2%
0.8314218191 1
 
< 0.1%
0.8136855693 2
 
< 0.1%
0.7947806078 10
 
< 0.1%
0.7897628137 321
0.5%
0.7833257361 6
 
< 0.1%
0.768762433 2
 
< 0.1%

exterior_color_x3
Real number (ℝ)

Distinct2394
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58784552
Minimum0
Maximum1
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:36.684043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33508261
Q10.53157696
median0.57866961
Q30.67574867
95-th percentile0.80536141
Maximum1
Range1
Interquartile range (IQR)0.14417171

Descriptive statistics

Standard deviation0.14492926
Coefficient of variation (CV)0.24654311
Kurtosis0.83916777
Mean0.58784552
Median Absolute Deviation (MAD)0.071098954
Skewness-0.52117434
Sum35844.469
Variance0.021004491
MonotonicityNot monotonic
2024-07-24T19:17:36.832608image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5786696107 3562
 
5.8%
0.5734664806 1746
 
2.9%
0.5342019448 1617
 
2.7%
0.8417314733 1484
 
2.4%
0.5559208177 1277
 
2.1%
0.6452719298 765
 
1.3%
0.5770113389 743
 
1.2%
0.3878432088 721
 
1.2%
0.5881059093 705
 
1.2%
0.5982390521 681
 
1.1%
Other values (2384) 47675
78.2%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.01067295797 5
 
< 0.1%
0.07072117142 4
 
< 0.1%
0.09347280586 187
0.3%
0.1210616207 10
 
< 0.1%
0.1313714559 54
 
0.1%
0.1316849936 84
 
0.1%
0.1333684577 341
0.6%
0.1348726913 1
 
< 0.1%
0.1363769249 8
 
< 0.1%
ValueCountFrequency (%)
1 7
 
< 0.1%
0.9879509127 60
 
0.1%
0.9759017869 20
 
< 0.1%
0.9436249707 2
 
< 0.1%
0.9208657463 107
 
0.2%
0.9088166205 21
 
< 0.1%
0.8791595856 656
1.1%
0.8729377228 255
 
0.4%
0.870240944 4
 
< 0.1%
0.8674852564 3
 
< 0.1%

exterior_color_x4
Real number (ℝ)

ZEROS 

Distinct2392
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55316791
Minimum0
Maximum1
Zeros1746
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:36.980729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17334817
Q10.41881119
median0.59891196
Q30.723476
95-th percentile0.82015157
Maximum1
Range1
Interquartile range (IQR)0.3046648

Descriptive statistics

Standard deviation0.21546514
Coefficient of variation (CV)0.38951127
Kurtosis-0.37373204
Mean0.55316791
Median Absolute Deviation (MAD)0.15197259
Skewness-0.59482558
Sum33729.966
Variance0.046425225
MonotonicityNot monotonic
2024-07-24T19:17:37.149546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8064669331 3562
 
5.8%
0 1746
 
2.9%
0.1733481686 1617
 
2.7%
0.3074349035 1484
 
2.4%
0.2436348734 1277
 
2.1%
0.7530042167 765
 
1.3%
0.6886186063 743
 
1.2%
0.8648592826 721
 
1.2%
0.7765061235 705
 
1.2%
0.7737971859 681
 
1.1%
Other values (2382) 47675
78.2%
ValueCountFrequency (%)
0 1746
2.9%
0.1107156014 33
 
0.1%
0.119764809 2
 
< 0.1%
0.1284918706 1
 
< 0.1%
0.1428398102 51
 
0.1%
0.1537174259 2
 
< 0.1%
0.1589172746 37
 
0.1%
0.1733481686 1617
2.7%
0.1864076272 4
 
< 0.1%
0.1929877032 169
 
0.3%
ValueCountFrequency (%)
1 5
 
< 0.1%
0.9858827189 10
 
< 0.1%
0.9717654636 13
 
< 0.1%
0.9603199161 10
 
< 0.1%
0.9511175998 11
 
< 0.1%
0.9464531786 8
 
< 0.1%
0.94255037 43
 
0.1%
0.9232516256 2
 
< 0.1%
0.9196482463 519
0.9%
0.9177272647 10
 
< 0.1%

interior_color_x0
Real number (ℝ)

Distinct1549
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30220699
Minimum0
Maximum1
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:37.328101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.20430064
Q10.22401969
median0.22401969
Q30.39076951
95-th percentile0.49630485
Maximum1
Range1
Interquartile range (IQR)0.16674982

Descriptive statistics

Standard deviation0.1143856
Coefficient of variation (CV)0.37850086
Kurtosis0.2470553
Mean0.30220699
Median Absolute Deviation (MAD)0.03444857
Skewness0.8823189
Sum18427.373
Variance0.013084066
MonotonicityNot monotonic
2024-07-24T19:17:37.501781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2240196922 24158
39.6%
0.2043006378 4745
 
7.8%
0.3907695134 3573
 
5.9%
0.2584682624 3302
 
5.4%
0.4963048528 2992
 
4.9%
0.4526886362 2772
 
4.5%
0.2829938781 1320
 
2.2%
0.2668474734 872
 
1.4%
0.4757433088 734
 
1.2%
0.3143896102 655
 
1.1%
Other values (1539) 15853
26.0%
ValueCountFrequency (%)
0 21
 
< 0.1%
0.04051975686 466
0.8%
0.05973864195 8
 
< 0.1%
0.0743124531 2
 
< 0.1%
0.09473083983 42
 
0.1%
0.09775409503 1
 
< 0.1%
0.1006738339 127
 
0.2%
0.1112450157 4
 
< 0.1%
0.1120098382 9
 
< 0.1%
0.1178083746 24
 
< 0.1%
ValueCountFrequency (%)
1 9
 
< 0.1%
0.8037925892 3
 
< 0.1%
0.7608130383 65
0.1%
0.7512255002 157
0.3%
0.7300266472 10
 
< 0.1%
0.7021034896 26
 
< 0.1%
0.6455651861 2
 
< 0.1%
0.6344710066 3
 
< 0.1%
0.6306070839 5
 
< 0.1%
0.6292341312 7
 
< 0.1%

interior_color_x1
Real number (ℝ)

Distinct1552
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35312591
Minimum0
Maximum1
Zeros41
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:37.661220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26159365
Q10.26159365
median0.29725261
Q30.38214039
95-th percentile0.78214334
Maximum1
Range1
Interquartile range (IQR)0.12054675

Descriptive statistics

Standard deviation0.14412443
Coefficient of variation (CV)0.40813893
Kurtosis3.5932344
Mean0.35312591
Median Absolute Deviation (MAD)0.035658965
Skewness2.0192821
Sum21532.205
Variance0.020771851
MonotonicityNot monotonic
2024-07-24T19:17:37.816610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2615936462 24158
39.6%
0.4186532493 4745
 
7.8%
0.7821433377 3598
 
5.9%
0.2674072079 3302
 
5.4%
0.3190295177 2992
 
4.9%
0.3544231145 2772
 
4.5%
0.3395451682 1320
 
2.2%
0.3419133311 872
 
1.4%
0.3751646142 734
 
1.2%
0.3492622087 655
 
1.1%
Other values (1542) 15828
26.0%
ValueCountFrequency (%)
0 41
0.1%
0.1000040242 100
0.2%
0.1045858267 76
0.1%
0.1296093571 3
 
< 0.1%
0.1305964349 12
 
< 0.1%
0.138533087 9
 
< 0.1%
0.1454699356 1
 
< 0.1%
0.1455563732 9
 
< 0.1%
0.1583320906 2
 
< 0.1%
0.1597926772 2
 
< 0.1%
ValueCountFrequency (%)
1 13
 
< 0.1%
0.9996599593 21
 
< 0.1%
0.9332067849 75
0.1%
0.9326607638 36
0.1%
0.8996550695 11
 
< 0.1%
0.8702771441 6
 
< 0.1%
0.8424309205 14
 
< 0.1%
0.8408271165 4
 
< 0.1%
0.8347006132 75
0.1%
0.8346273569 2
 
< 0.1%

interior_color_x2
Real number (ℝ)

Distinct1553
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46177227
Minimum0
Maximum1
Zeros127
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:37.976547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32059219
Q10.4024697
median0.51280296
Q30.52203401
95-th percentile0.52230095
Maximum1
Range1
Interquartile range (IQR)0.11956431

Descriptive statistics

Standard deviation0.086659389
Coefficient of variation (CV)0.18766694
Kurtosis2.8272574
Mean0.46177227
Median Absolute Deviation (MAD)0.0094979982
Skewness-1.0985419
Sum28157.026
Variance0.0075098496
MonotonicityNot monotonic
2024-07-24T19:17:38.143613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5220340124 24158
39.6%
0.4319095428 4745
 
7.8%
0.5223009535 3598
 
5.9%
0.5035718982 3302
 
5.4%
0.3479474777 2992
 
4.9%
0.3709869679 2772
 
4.5%
0.385201712 1320
 
2.2%
0.4165066954 872
 
1.4%
0.3534324559 734
 
1.2%
0.402469703 655
 
1.1%
Other values (1543) 15828
26.0%
ValueCountFrequency (%)
0 127
 
0.2%
0.07691011828 12
 
< 0.1%
0.08773159045 1
 
< 0.1%
0.1413944154 1
 
< 0.1%
0.1518300191 2
 
< 0.1%
0.1538202124 533
0.9%
0.1563165649 2
 
< 0.1%
0.1608750124 2
 
< 0.1%
0.1629204591 1
 
< 0.1%
0.1646416967 32
 
0.1%
ValueCountFrequency (%)
1 21
 
< 0.1%
0.7780716204 42
0.1%
0.7610170183 9
 
< 0.1%
0.758187276 1
 
< 0.1%
0.7525277431 41
0.1%
0.7517859491 2
 
< 0.1%
0.7411710375 2
 
< 0.1%
0.7239382977 4
 
< 0.1%
0.7063619498 100
0.2%
0.7034809112 2
 
< 0.1%

interior_color_x3
Real number (ℝ)

Distinct1550
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72433974
Minimum0
Maximum1
Zeros75
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:38.304514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2795023
Q10.73079802
median0.77259296
Q30.80215164
95-th percentile0.93617018
Maximum1
Range1
Interquartile range (IQR)0.071353621

Descriptive statistics

Standard deviation0.1675885
Coefficient of variation (CV)0.23136726
Kurtosis2.2096053
Mean0.72433974
Median Absolute Deviation (MAD)0.034499806
Skewness-1.6402014
Sum44167.34
Variance0.028085907
MonotonicityNot monotonic
2024-07-24T19:17:38.474100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.772592957 24158
39.6%
0.8221623053 4745
 
7.8%
0.2795022973 3573
 
5.9%
0.8971832299 3302
 
5.4%
0.9361701828 2992
 
4.9%
0.7380931509 2772
 
4.5%
0.8482673765 1320
 
2.2%
0.8021516412 872
 
1.4%
0.7307980199 734
 
1.2%
0.7924446681 655
 
1.1%
Other values (1540) 15853
26.0%
ValueCountFrequency (%)
0 75
0.1%
0.06440962919 36
0.1%
0.11385711 2
 
< 0.1%
0.1161543575 2
 
< 0.1%
0.1261453859 4
 
< 0.1%
0.1305368166 2
 
< 0.1%
0.1522464858 42
0.1%
0.1527440923 4
 
< 0.1%
0.1644532558 2
 
< 0.1%
0.1670158715 4
 
< 0.1%
ValueCountFrequency (%)
1 4
 
< 0.1%
0.9941366877 32
0.1%
0.9939780386 3
 
< 0.1%
0.9920773753 1
 
< 0.1%
0.9913775183 3
 
< 0.1%
0.9889654805 6
 
< 0.1%
0.9884345227 3
 
< 0.1%
0.987230013 2
 
< 0.1%
0.9870112079 4
 
< 0.1%
0.9844245226 2
 
< 0.1%

interior_color_x4
Real number (ℝ)

Distinct1552
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24388922
Minimum0
Maximum1
Zeros139
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:38.628001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.031200109
Q10.20140251
median0.20140251
Q30.25301033
95-th percentile0.54739773
Maximum1
Range1
Interquartile range (IQR)0.051607814

Descriptive statistics

Standard deviation0.13420834
Coefficient of variation (CV)0.55028403
Kurtosis4.5506107
Mean0.24388922
Median Absolute Deviation (MAD)0.032315456
Skewness1.8491367
Sum14871.389
Variance0.018011879
MonotonicityNot monotonic
2024-07-24T19:17:38.784690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2014025119 24158
39.6%
0.2530103255 4745
 
7.8%
0.03120010878 3598
 
5.9%
0.5473977304 3302
 
5.4%
0.1615776436 2992
 
4.9%
0.219333092 2772
 
4.5%
0.2402693816 1320
 
2.2%
0.2425314602 872
 
1.4%
0.2413977738 734
 
1.2%
0.2453223437 655
 
1.1%
Other values (1542) 15828
26.0%
ValueCountFrequency (%)
0 139
 
0.2%
0.01521279629 17
 
< 0.1%
0.01943098939 75
 
0.1%
0.03120010878 3598
5.9%
0.03586103552 36
 
0.1%
0.04849862498 2
 
< 0.1%
0.05048547079 6
 
< 0.1%
0.05141845394 1
 
< 0.1%
0.05691392969 11
 
< 0.1%
0.05744896732 4
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.8834132131 272
0.4%
0.8462398959 10
 
< 0.1%
0.8039999192 6
 
< 0.1%
0.7994093571 1
 
< 0.1%
0.7948189122 76
 
0.1%
0.7858895634 5
 
< 0.1%
0.7672682065 2
 
< 0.1%
0.7672535998 1
 
< 0.1%
0.7597438612 1
 
< 0.1%

drivetrain_All-wheel Drive
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
1.0
47993 
0.0
12983 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 47993
78.7%
0.0 12983
 
21.3%

Length

2024-07-24T19:17:38.922591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:39.027853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 47993
78.7%
0.0 12983
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 73959
40.4%
. 60976
33.3%
1 47993
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73959
40.4%
. 60976
33.3%
1 47993
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73959
40.4%
. 60976
33.3%
1 47993
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73959
40.4%
. 60976
33.3%
1 47993
26.2%

drivetrain_Front-wheel Drive
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
48935 
1.0
12041 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 48935
80.3%
1.0 12041
 
19.7%

Length

2024-07-24T19:17:39.147846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:39.512626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48935
80.3%
1.0 12041
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 109911
60.1%
. 60976
33.3%
1 12041
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109911
60.1%
. 60976
33.3%
1 12041
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109911
60.1%
. 60976
33.3%
1 12041
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109911
60.1%
. 60976
33.3%
1 12041
 
6.6%

drivetrain_Rear-wheel Drive
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60173 
1.0
 
803

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60173
98.7%
1.0 803
 
1.3%

Length

2024-07-24T19:17:39.636352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:39.746635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60173
98.7%
1.0 803
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 121149
66.2%
. 60976
33.3%
1 803
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121149
66.2%
. 60976
33.3%
1 803
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121149
66.2%
. 60976
33.3%
1 803
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121149
66.2%
. 60976
33.3%
1 803
 
0.4%

drivetrain_nan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60837 
1.0
 
139

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60837
99.8%
1.0 139
 
0.2%

Length

2024-07-24T19:17:39.852920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:39.953758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60837
99.8%
1.0 139
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

make_Acura
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59726 
1.0
 
1250

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59726
98.0%
1.0 1250
 
2.0%

Length

2024-07-24T19:17:40.060730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:40.166229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59726
98.0%
1.0 1250
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 120702
66.0%
. 60976
33.3%
1 1250
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120702
66.0%
. 60976
33.3%
1 1250
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120702
66.0%
. 60976
33.3%
1 1250
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120702
66.0%
. 60976
33.3%
1 1250
 
0.7%

make_Alfa Romeo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60696 
1.0
 
280

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60696
99.5%
1.0 280
 
0.5%

Length

2024-07-24T19:17:40.271728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:40.404867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60696
99.5%
1.0 280
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 121672
66.5%
. 60976
33.3%
1 280
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121672
66.5%
. 60976
33.3%
1 280
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121672
66.5%
. 60976
33.3%
1 280
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121672
66.5%
. 60976
33.3%
1 280
 
0.2%

make_Audi
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
58605 
1.0
 
2371

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 58605
96.1%
1.0 2371
 
3.9%

Length

2024-07-24T19:17:40.661414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:40.812710image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 58605
96.1%
1.0 2371
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 119581
65.4%
. 60976
33.3%
1 2371
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119581
65.4%
. 60976
33.3%
1 2371
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119581
65.4%
. 60976
33.3%
1 2371
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119581
65.4%
. 60976
33.3%
1 2371
 
1.3%

make_BMW
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
58475 
1.0
 
2501

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 58475
95.9%
1.0 2501
 
4.1%

Length

2024-07-24T19:17:40.973699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:41.104283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 58475
95.9%
1.0 2501
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 119451
65.3%
. 60976
33.3%
1 2501
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119451
65.3%
. 60976
33.3%
1 2501
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119451
65.3%
. 60976
33.3%
1 2501
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119451
65.3%
. 60976
33.3%
1 2501
 
1.4%

make_Buick
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59823 
1.0
 
1153

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59823
98.1%
1.0 1153
 
1.9%

Length

2024-07-24T19:17:41.237789image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:41.350455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59823
98.1%
1.0 1153
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 120799
66.0%
. 60976
33.3%
1 1153
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120799
66.0%
. 60976
33.3%
1 1153
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120799
66.0%
. 60976
33.3%
1 1153
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120799
66.0%
. 60976
33.3%
1 1153
 
0.6%

make_Cadillac
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59496 
1.0
 
1480

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59496
97.6%
1.0 1480
 
2.4%

Length

2024-07-24T19:17:41.465641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:41.581332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59496
97.6%
1.0 1480
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 120472
65.9%
. 60976
33.3%
1 1480
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120472
65.9%
. 60976
33.3%
1 1480
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120472
65.9%
. 60976
33.3%
1 1480
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120472
65.9%
. 60976
33.3%
1 1480
 
0.8%

make_Chevrolet
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
55292 
1.0
5684 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 55292
90.7%
1.0 5684
 
9.3%

Length

2024-07-24T19:17:41.709192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:41.824991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 55292
90.7%
1.0 5684
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 116268
63.6%
. 60976
33.3%
1 5684
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116268
63.6%
. 60976
33.3%
1 5684
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116268
63.6%
. 60976
33.3%
1 5684
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116268
63.6%
. 60976
33.3%
1 5684
 
3.1%

make_Chrysler
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60755 
1.0
 
221

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60755
99.6%
1.0 221
 
0.4%

Length

2024-07-24T19:17:41.949535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:42.058846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60755
99.6%
1.0 221
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 121731
66.5%
. 60976
33.3%
1 221
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121731
66.5%
. 60976
33.3%
1 221
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121731
66.5%
. 60976
33.3%
1 221
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121731
66.5%
. 60976
33.3%
1 221
 
0.1%

make_Dodge
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59822 
1.0
 
1154

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59822
98.1%
1.0 1154
 
1.9%

Length

2024-07-24T19:17:42.184364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:42.299767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59822
98.1%
1.0 1154
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 120798
66.0%
. 60976
33.3%
1 1154
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120798
66.0%
. 60976
33.3%
1 1154
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120798
66.0%
. 60976
33.3%
1 1154
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120798
66.0%
. 60976
33.3%
1 1154
 
0.6%

make_Ford
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
55443 
1.0
 
5533

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 55443
90.9%
1.0 5533
 
9.1%

Length

2024-07-24T19:17:42.426951image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:42.543345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 55443
90.9%
1.0 5533
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 116419
63.6%
. 60976
33.3%
1 5533
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116419
63.6%
. 60976
33.3%
1 5533
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116419
63.6%
. 60976
33.3%
1 5533
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116419
63.6%
. 60976
33.3%
1 5533
 
3.0%

make_GMC
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59313 
1.0
 
1663

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59313
97.3%
1.0 1663
 
2.7%

Length

2024-07-24T19:17:42.670648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:42.783713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59313
97.3%
1.0 1663
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 120289
65.8%
. 60976
33.3%
1 1663
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120289
65.8%
. 60976
33.3%
1 1663
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120289
65.8%
. 60976
33.3%
1 1663
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120289
65.8%
. 60976
33.3%
1 1663
 
0.9%

make_Genesis
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60608 
1.0
 
368

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60608
99.4%
1.0 368
 
0.6%

Length

2024-07-24T19:17:42.906490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:43.016258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60608
99.4%
1.0 368
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 121584
66.5%
. 60976
33.3%
1 368
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121584
66.5%
. 60976
33.3%
1 368
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121584
66.5%
. 60976
33.3%
1 368
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121584
66.5%
. 60976
33.3%
1 368
 
0.2%

make_Honda
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
58116 
1.0
 
2860

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 58116
95.3%
1.0 2860
 
4.7%

Length

2024-07-24T19:17:43.133795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:43.256032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 58116
95.3%
1.0 2860
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 119092
65.1%
. 60976
33.3%
1 2860
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119092
65.1%
. 60976
33.3%
1 2860
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119092
65.1%
. 60976
33.3%
1 2860
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119092
65.1%
. 60976
33.3%
1 2860
 
1.6%

make_Hyundai
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
57551 
1.0
 
3425

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 57551
94.4%
1.0 3425
 
5.6%

Length

2024-07-24T19:17:43.383744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:43.500163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 57551
94.4%
1.0 3425
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 118527
64.8%
. 60976
33.3%
1 3425
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118527
64.8%
. 60976
33.3%
1 3425
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118527
64.8%
. 60976
33.3%
1 3425
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118527
64.8%
. 60976
33.3%
1 3425
 
1.9%

make_INFINITI
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60168 
1.0
 
808

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60168
98.7%
1.0 808
 
1.3%

Length

2024-07-24T19:17:43.619459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:43.730641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60168
98.7%
1.0 808
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 121144
66.2%
. 60976
33.3%
1 808
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121144
66.2%
. 60976
33.3%
1 808
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121144
66.2%
. 60976
33.3%
1 808
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121144
66.2%
. 60976
33.3%
1 808
 
0.4%

make_Jaguar
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60736 
1.0
 
240

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60736
99.6%
1.0 240
 
0.4%

Length

2024-07-24T19:17:43.844670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:43.948290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60736
99.6%
1.0 240
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 121712
66.5%
. 60976
33.3%
1 240
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121712
66.5%
. 60976
33.3%
1 240
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121712
66.5%
. 60976
33.3%
1 240
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121712
66.5%
. 60976
33.3%
1 240
 
0.1%

make_Jeep
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
55857 
1.0
 
5119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 55857
91.6%
1.0 5119
 
8.4%

Length

2024-07-24T19:17:44.061435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:44.167060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 55857
91.6%
1.0 5119
 
8.4%

Most occurring characters

ValueCountFrequency (%)
0 116833
63.9%
. 60976
33.3%
1 5119
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116833
63.9%
. 60976
33.3%
1 5119
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116833
63.9%
. 60976
33.3%
1 5119
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116833
63.9%
. 60976
33.3%
1 5119
 
2.8%

make_Kia
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
58791 
1.0
 
2185

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 58791
96.4%
1.0 2185
 
3.6%

Length

2024-07-24T19:17:44.283680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:44.407853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 58791
96.4%
1.0 2185
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 119767
65.5%
. 60976
33.3%
1 2185
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119767
65.5%
. 60976
33.3%
1 2185
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119767
65.5%
. 60976
33.3%
1 2185
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119767
65.5%
. 60976
33.3%
1 2185
 
1.2%

make_Land Rover
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60095 
1.0
 
881

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60095
98.6%
1.0 881
 
1.4%

Length

2024-07-24T19:17:44.527043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:44.647846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60095
98.6%
1.0 881
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 121071
66.2%
. 60976
33.3%
1 881
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121071
66.2%
. 60976
33.3%
1 881
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121071
66.2%
. 60976
33.3%
1 881
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121071
66.2%
. 60976
33.3%
1 881
 
0.5%

make_Lexus
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59744 
1.0
 
1232

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59744
98.0%
1.0 1232
 
2.0%

Length

2024-07-24T19:17:44.768712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:44.881016image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59744
98.0%
1.0 1232
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 120720
66.0%
. 60976
33.3%
1 1232
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120720
66.0%
. 60976
33.3%
1 1232
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120720
66.0%
. 60976
33.3%
1 1232
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120720
66.0%
. 60976
33.3%
1 1232
 
0.7%

make_Lincoln
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59838 
1.0
 
1138

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59838
98.1%
1.0 1138
 
1.9%

Length

2024-07-24T19:17:45.006616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:45.126090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59838
98.1%
1.0 1138
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 120814
66.0%
. 60976
33.3%
1 1138
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120814
66.0%
. 60976
33.3%
1 1138
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120814
66.0%
. 60976
33.3%
1 1138
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120814
66.0%
. 60976
33.3%
1 1138
 
0.6%

make_Mazda
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
58742 
1.0
 
2234

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 58742
96.3%
1.0 2234
 
3.7%

Length

2024-07-24T19:17:45.252219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:45.368901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 58742
96.3%
1.0 2234
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 119718
65.4%
. 60976
33.3%
1 2234
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119718
65.4%
. 60976
33.3%
1 2234
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119718
65.4%
. 60976
33.3%
1 2234
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119718
65.4%
. 60976
33.3%
1 2234
 
1.2%

make_Mercedes-Benz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
57781 
1.0
 
3195

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 57781
94.8%
1.0 3195
 
5.2%

Length

2024-07-24T19:17:45.489580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:45.609587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 57781
94.8%
1.0 3195
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 118757
64.9%
. 60976
33.3%
1 3195
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118757
64.9%
. 60976
33.3%
1 3195
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118757
64.9%
. 60976
33.3%
1 3195
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118757
64.9%
. 60976
33.3%
1 3195
 
1.7%

make_Nissan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
56979 
1.0
 
3997

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 56979
93.4%
1.0 3997
 
6.6%

Length

2024-07-24T19:17:45.728102image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:45.841624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 56979
93.4%
1.0 3997
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 117955
64.5%
. 60976
33.3%
1 3997
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 117955
64.5%
. 60976
33.3%
1 3997
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 117955
64.5%
. 60976
33.3%
1 3997
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 117955
64.5%
. 60976
33.3%
1 3997
 
2.2%

make_Porsche
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60676 
1.0
 
300

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60676
99.5%
1.0 300
 
0.5%

Length

2024-07-24T19:17:45.965456image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:46.077934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60676
99.5%
1.0 300
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 121652
66.5%
. 60976
33.3%
1 300
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121652
66.5%
. 60976
33.3%
1 300
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121652
66.5%
. 60976
33.3%
1 300
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121652
66.5%
. 60976
33.3%
1 300
 
0.2%

make_RAM
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59971 
1.0
 
1005

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59971
98.4%
1.0 1005
 
1.6%

Length

2024-07-24T19:17:46.379513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:46.489761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59971
98.4%
1.0 1005
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 120947
66.1%
. 60976
33.3%
1 1005
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120947
66.1%
. 60976
33.3%
1 1005
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120947
66.1%
. 60976
33.3%
1 1005
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120947
66.1%
. 60976
33.3%
1 1005
 
0.5%

make_Subaru
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
58508 
1.0
 
2468

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 58508
96.0%
1.0 2468
 
4.0%

Length

2024-07-24T19:17:46.602526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:46.708646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 58508
96.0%
1.0 2468
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 119484
65.3%
. 60976
33.3%
1 2468
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119484
65.3%
. 60976
33.3%
1 2468
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119484
65.3%
. 60976
33.3%
1 2468
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119484
65.3%
. 60976
33.3%
1 2468
 
1.3%

make_Tesla
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60837 
1.0
 
139

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60837
99.8%
1.0 139
 
0.2%

Length

2024-07-24T19:17:46.828896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:46.932839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60837
99.8%
1.0 139
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121813
66.6%
. 60976
33.3%
1 139
 
0.1%

make_Toyota
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
58669 
1.0
 
2307

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 58669
96.2%
1.0 2307
 
3.8%

Length

2024-07-24T19:17:47.051624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:47.163972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 58669
96.2%
1.0 2307
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 119645
65.4%
. 60976
33.3%
1 2307
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119645
65.4%
. 60976
33.3%
1 2307
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119645
65.4%
. 60976
33.3%
1 2307
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119645
65.4%
. 60976
33.3%
1 2307
 
1.3%

make_Volkswagen
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
57861 
1.0
 
3115

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 57861
94.9%
1.0 3115
 
5.1%

Length

2024-07-24T19:17:47.272820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:47.370731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 57861
94.9%
1.0 3115
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 118837
65.0%
. 60976
33.3%
1 3115
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118837
65.0%
. 60976
33.3%
1 3115
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118837
65.0%
. 60976
33.3%
1 3115
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118837
65.0%
. 60976
33.3%
1 3115
 
1.7%

make_Volvo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60306 
1.0
 
670

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60306
98.9%
1.0 670
 
1.1%

Length

2024-07-24T19:17:47.482775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:47.594109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60306
98.9%
1.0 670
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 121282
66.3%
. 60976
33.3%
1 670
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121282
66.3%
. 60976
33.3%
1 670
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121282
66.3%
. 60976
33.3%
1 670
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121282
66.3%
. 60976
33.3%
1 670
 
0.4%

bodystyle_Cargo Van
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60719 
1.0
 
257

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60719
99.6%
1.0 257
 
0.4%

Length

2024-07-24T19:17:47.708711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:47.811418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60719
99.6%
1.0 257
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 121695
66.5%
. 60976
33.3%
1 257
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121695
66.5%
. 60976
33.3%
1 257
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121695
66.5%
. 60976
33.3%
1 257
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121695
66.5%
. 60976
33.3%
1 257
 
0.1%

bodystyle_Convertible
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60635 
1.0
 
341

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60635
99.4%
1.0 341
 
0.6%

Length

2024-07-24T19:17:47.923553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:48.043922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60635
99.4%
1.0 341
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 121611
66.5%
. 60976
33.3%
1 341
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121611
66.5%
. 60976
33.3%
1 341
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121611
66.5%
. 60976
33.3%
1 341
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121611
66.5%
. 60976
33.3%
1 341
 
0.2%

bodystyle_Coupe
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60510 
1.0
 
466

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60510
99.2%
1.0 466
 
0.8%

Length

2024-07-24T19:17:48.163033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:48.281687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60510
99.2%
1.0 466
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 121486
66.4%
. 60976
33.3%
1 466
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121486
66.4%
. 60976
33.3%
1 466
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121486
66.4%
. 60976
33.3%
1 466
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121486
66.4%
. 60976
33.3%
1 466
 
0.3%

bodystyle_Hatchback
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60226 
1.0
 
750

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60226
98.8%
1.0 750
 
1.2%

Length

2024-07-24T19:17:48.401376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:48.513483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60226
98.8%
1.0 750
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 121202
66.3%
. 60976
33.3%
1 750
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121202
66.3%
. 60976
33.3%
1 750
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121202
66.3%
. 60976
33.3%
1 750
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121202
66.3%
. 60976
33.3%
1 750
 
0.4%

bodystyle_Minivan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60843 
1.0
 
133

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60843
99.8%
1.0 133
 
0.2%

Length

2024-07-24T19:17:48.641013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:48.751765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60843
99.8%
1.0 133
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 121819
66.6%
. 60976
33.3%
1 133
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121819
66.6%
. 60976
33.3%
1 133
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121819
66.6%
. 60976
33.3%
1 133
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121819
66.6%
. 60976
33.3%
1 133
 
0.1%

bodystyle_Passenger Van
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60725 
1.0
 
251

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60725
99.6%
1.0 251
 
0.4%

Length

2024-07-24T19:17:48.865469image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:48.968941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60725
99.6%
1.0 251
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 121701
66.5%
. 60976
33.3%
1 251
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121701
66.5%
. 60976
33.3%
1 251
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121701
66.5%
. 60976
33.3%
1 251
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121701
66.5%
. 60976
33.3%
1 251
 
0.1%

bodystyle_Pickup Truck
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
55617 
1.0
 
5359

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 55617
91.2%
1.0 5359
 
8.8%

Length

2024-07-24T19:17:49.079863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:49.196908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 55617
91.2%
1.0 5359
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 116593
63.7%
. 60976
33.3%
1 5359
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116593
63.7%
. 60976
33.3%
1 5359
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116593
63.7%
. 60976
33.3%
1 5359
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116593
63.7%
. 60976
33.3%
1 5359
 
2.9%

bodystyle_SUV
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
1.0
43816 
0.0
17160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 43816
71.9%
0.0 17160
 
28.1%

Length

2024-07-24T19:17:49.316036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:49.434891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 43816
71.9%
0.0 17160
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 78136
42.7%
. 60976
33.3%
1 43816
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 78136
42.7%
. 60976
33.3%
1 43816
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 78136
42.7%
. 60976
33.3%
1 43816
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 78136
42.7%
. 60976
33.3%
1 43816
24.0%

bodystyle_Sedan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
51647 
1.0
9329 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 51647
84.7%
1.0 9329
 
15.3%

Length

2024-07-24T19:17:49.556051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:49.670515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 51647
84.7%
1.0 9329
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 112623
61.6%
. 60976
33.3%
1 9329
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112623
61.6%
. 60976
33.3%
1 9329
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112623
61.6%
. 60976
33.3%
1 9329
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112623
61.6%
. 60976
33.3%
1 9329
 
5.1%

bodystyle_Wagon
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60839 
1.0
 
137

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60839
99.8%
1.0 137
 
0.2%

Length

2024-07-24T19:17:49.792313image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:49.891950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60839
99.8%
1.0 137
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

bodystyle_nan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60839 
1.0
 
137

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60839
99.8%
1.0 137
 
0.2%

Length

2024-07-24T19:17:50.013568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:50.124676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60839
99.8%
1.0 137
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121815
66.6%
. 60976
33.3%
1 137
 
0.1%

cat_x0
Real number (ℝ)

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44208461
Minimum0
Maximum1
Zeros163
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:50.248169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.40885761
Q10.42102898
median0.44081174
Q30.44389341
95-th percentile0.59594911
Maximum1
Range1
Interquartile range (IQR)0.022864434

Descriptive statistics

Standard deviation0.068842357
Coefficient of variation (CV)0.15572213
Kurtosis19.465924
Mean0.44208461
Median Absolute Deviation (MAD)0.014641821
Skewness-0.22039012
Sum26956.551
Variance0.0047392701
MonotonicityNot monotonic
2024-07-24T19:17:50.385130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.4408117361 13802
22.6%
0.4261699155 10584
17.4%
0.4210289809 6551
10.7%
0.4088576081 6031
9.9%
0.4837759592 4074
 
6.7%
0.4438934147 3707
 
6.1%
0.424110629 2712
 
4.4%
0.6079612342 2218
 
3.6%
0.4516191357 1918
 
3.1%
0.4420892759 1734
 
2.8%
Other values (25) 7645
12.5%
ValueCountFrequency (%)
0 163
 
0.3%
0.007646909011 136
 
0.2%
0.05211924123 110
 
0.2%
0.06305927741 178
 
0.3%
0.34499408 442
0.7%
0.3484213698 516
0.8%
0.3531083082 360
0.6%
0.3551627403 33
 
0.1%
0.3662968218 226
0.4%
0.3671484845 77
 
0.1%
ValueCountFrequency (%)
1 79
 
0.1%
0.932003702 39
 
0.1%
0.6574724674 358
 
0.6%
0.6079612342 2218
3.6%
0.6046979571 327
 
0.5%
0.5959491112 1352
 
2.2%
0.5129771665 73
 
0.1%
0.4837759592 4074
6.7%
0.4777298104 58
 
0.1%
0.4703271076 1259
 
2.1%

cat_x1
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68069433
Minimum0
Maximum1
Zeros2218
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:50.528302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0022405777
Q10.6661176
median0.72378739
Q30.74992393
95-th percentile0.78905485
Maximum1
Range1
Interquartile range (IQR)0.083806328

Descriptive statistics

Standard deviation0.18254071
Coefficient of variation (CV)0.2681684
Kurtosis8.5807243
Mean0.68069433
Median Absolute Deviation (MAD)0.029967878
Skewness-2.8998957
Sum41506.017
Variance0.03332111
MonotonicityNot monotonic
2024-07-24T19:17:50.678825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.7237873879 13802
22.6%
0.7162371709 10584
17.4%
0.6587252598 6551
10.7%
0.7886503887 6031
9.9%
0.7499239257 4074
 
6.7%
0.7537552661 3707
 
6.1%
0.6886931381 2712
 
4.4%
0 2218
 
3.6%
0.6365176467 1918
 
3.1%
0.6036364424 1734
 
2.8%
Other values (25) 7645
12.5%
ValueCountFrequency (%)
0 2218
3.6%
0.002240577668 1352
2.2%
0.1835926872 58
 
0.1%
0.1850863736 19
 
< 0.1%
0.2276708815 6
 
< 0.1%
0.2464812025 18
 
< 0.1%
0.5036791918 217
 
0.4%
0.5571769425 178
 
0.3%
0.5860169661 77
 
0.1%
0.5939667393 136
 
0.2%
ValueCountFrequency (%)
1 1259
 
2.1%
0.8461622917 1534
 
2.5%
0.7999846796 39
 
0.1%
0.7890548494 327
 
0.5%
0.7886503887 6031
9.9%
0.7619453248 79
 
0.1%
0.7537552661 3707
6.1%
0.7537427177 73
 
0.1%
0.7499239257 4074
6.7%
0.747700871 516
 
0.8%

cat_x2
Real number (ℝ)

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49946945
Minimum0
Maximum1
Zeros39
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size952.8 KiB
2024-07-24T19:17:50.829775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.031835517
Q10.46572071
median0.56829489
Q30.58346325
95-th percentile0.62627165
Maximum1
Range1
Interquartile range (IQR)0.11774254

Descriptive statistics

Standard deviation0.18316358
Coefficient of variation (CV)0.36671629
Kurtosis2.0292345
Mean0.49946945
Median Absolute Deviation (MAD)0.029490303
Skewness-1.6683013
Sum30455.649
Variance0.033548899
MonotonicityNot monotonic
2024-07-24T19:17:50.973320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.5682948884 13802
22.6%
0.6262716497 10584
17.4%
0.5834632472 6551
10.7%
0.4657207081 6031
9.9%
0.03183551674 4074
 
6.7%
0.5826168322 3707
 
6.1%
0.597785191 2712
 
4.4%
0.546299814 2218
 
3.6%
0.2537633054 1918
 
3.1%
0.03528134372 1734
 
2.8%
Other values (25) 7645
12.5%
ValueCountFrequency (%)
0 39
 
0.1%
0.03183551674 4074
6.7%
0.03528134372 1734
2.8%
0.03839439292 327
 
0.5%
0.0680362554 358
 
0.6%
0.07144274757 73
 
0.1%
0.09026395622 79
 
0.1%
0.1673458266 217
 
0.4%
0.2200012347 35
 
0.1%
0.2222984228 77
 
0.1%
ValueCountFrequency (%)
1 178
 
0.3%
0.994582393 136
 
0.2%
0.8209888238 163
 
0.3%
0.8138133421 18
 
< 0.1%
0.6553697243 110
 
0.2%
0.6262716497 10584
17.4%
0.597785191 2712
 
4.4%
0.5973010212 33
 
0.1%
0.5877530587 360
 
0.6%
0.5871887746 30
 
< 0.1%

fuel_type_Electric
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59107 
1.0
 
1869

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59107
96.9%
1.0 1869
 
3.1%

Length

2024-07-24T19:17:51.110594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:51.213214image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59107
96.9%
1.0 1869
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 120083
65.6%
. 60976
33.3%
1 1869
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120083
65.6%
. 60976
33.3%
1 1869
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120083
65.6%
. 60976
33.3%
1 1869
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120083
65.6%
. 60976
33.3%
1 1869
 
1.0%

fuel_type_Flexible
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
60815 
1.0
 
161

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 60815
99.7%
1.0 161
 
0.3%

Length

2024-07-24T19:17:51.323796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:51.422142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 60815
99.7%
1.0 161
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 121791
66.6%
. 60976
33.3%
1 161
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121791
66.6%
. 60976
33.3%
1 161
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121791
66.6%
. 60976
33.3%
1 161
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121791
66.6%
. 60976
33.3%
1 161
 
0.1%

fuel_type_Gasoline
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
1.0
57178 
0.0
 
3798

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 57178
93.8%
0.0 3798
 
6.2%

Length

2024-07-24T19:17:51.530017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:51.629561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 57178
93.8%
0.0 3798
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 64774
35.4%
. 60976
33.3%
1 57178
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 64774
35.4%
. 60976
33.3%
1 57178
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 64774
35.4%
. 60976
33.3%
1 57178
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 64774
35.4%
. 60976
33.3%
1 57178
31.3%

fuel_type_Hybrid
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size952.8 KiB
0.0
59208 
1.0
 
1768

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters182928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59208
97.1%
1.0 1768
 
2.9%

Length

2024-07-24T19:17:51.736333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-24T19:17:51.838617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59208
97.1%
1.0 1768
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 120184
65.7%
. 60976
33.3%
1 1768
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120184
65.7%
. 60976
33.3%
1 1768
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120184
65.7%
. 60976
33.3%
1 1768
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120184
65.7%
. 60976
33.3%
1 1768
 
1.0%

Interactions

2024-07-24T19:17:19.096336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:52.919454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:54.794906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:56.471312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:58.096618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:00.058705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:01.792870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:03.514684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:05.415001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:07.090494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:08.941071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:10.860880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:12.495477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:14.095049image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:15.732182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:17.485981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:19.194259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:53.086496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:54.902760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:56.571173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:58.209719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:00.161310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:01.899229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:03.617811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:05.519619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:07.199386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:09.052024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:10.967012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:12.592276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:14.193765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:15.970550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:17.587088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:19.295030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:53.205266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:55.006984image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:56.671241image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:58.330479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:00.268336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:02.001571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:03.722529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:05.628181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:07.308096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:09.160681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:11.076153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:12.696308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:14.297971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:16.062947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:17.680943image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:19.395145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:53.306229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:55.108191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:56.769690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:58.435529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:00.373666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:02.109707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:03.824607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:05.726815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:07.407923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:09.268155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:11.184829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:12.793656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:14.393621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:16.158009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:17.779877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:19.496921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:53.398946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:55.213492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:56.863738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:58.550055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:00.482203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:02.222606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:03.928611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:05.835598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:07.506836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:09.371580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:11.290815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:12.893130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:14.497111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:16.268821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:17.879873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:19.596676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:53.502836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:55.313761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:56.959344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:58.674384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:00.588599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:02.326380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:04.033378image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:05.939446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:07.608643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:09.486752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:11.390193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:12.997747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:14.596169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:16.375648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:17.979620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:19.691700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:53.604965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:55.420016image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:57.064728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:16:58.785585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:00.694209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:02.421801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-07-24T19:17:17.389350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-24T19:17:18.993182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-07-24T19:17:21.108621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-24T19:17:21.995996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearpricemileagestock_typemodel_hashed_0model_hashed_1model_hashed_2model_hashed_3model_hashed_4model_hashed_5model_hashed_6model_hashed_7model_hashed_8model_hashed_9model_hashed_10model_hashed_11model_hashed_12model_hashed_13model_hashed_14model_hashed_15model_hashed_16model_hashed_17model_hashed_18model_hashed_19model_hashed_20model_hashed_21model_hashed_22model_hashed_23model_hashed_24model_hashed_25model_hashed_26model_hashed_27model_hashed_28model_hashed_29model_hashed_30model_hashed_31model_hashed_32model_hashed_33model_hashed_34model_hashed_35model_hashed_36model_hashed_37model_hashed_38model_hashed_39model_hashed_40model_hashed_41model_hashed_42model_hashed_43model_hashed_44model_hashed_45model_hashed_46exterior_color_x0exterior_color_x1exterior_color_x2exterior_color_x3exterior_color_x4interior_color_x0interior_color_x1interior_color_x2interior_color_x3interior_color_x4drivetrain_All-wheel Drivedrivetrain_Front-wheel Drivedrivetrain_Rear-wheel Drivedrivetrain_nanmake_Acuramake_Alfa Romeomake_Audimake_BMWmake_Buickmake_Cadillacmake_Chevroletmake_Chryslermake_Dodgemake_Fordmake_GMCmake_Genesismake_Hondamake_Hyundaimake_INFINITImake_Jaguarmake_Jeepmake_Kiamake_Land Rovermake_Lexusmake_Lincolnmake_Mazdamake_Mercedes-Benzmake_Nissanmake_Porschemake_RAMmake_Subarumake_Teslamake_Toyotamake_Volkswagenmake_Volvobodystyle_Cargo Vanbodystyle_Convertiblebodystyle_Coupebodystyle_Hatchbackbodystyle_Minivanbodystyle_Passenger Vanbodystyle_Pickup Truckbodystyle_SUVbodystyle_Sedanbodystyle_Wagonbodystyle_nancat_x0cat_x1cat_x2fuel_type_Electricfuel_type_Flexiblefuel_type_Gasolinefuel_type_Hybrid
msrp
0.0672170.9818180.0732170.0000161.00.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.8359040.1588870.6119400.4247670.3521450.2240200.2615940.5220340.7725930.2014030.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.4438930.7537550.5826170.00.01.00.0
0.0867690.8727270.0777870.0113490.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.6460490.1603830.6462760.5786700.8064670.2240200.2615940.5220340.7725930.2014031.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.0000000.6122190.8209890.01.00.00.0
0.0364580.7818180.0364930.1921140.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5925170.3778110.2449040.4070630.6027710.2584680.2674070.5035720.8971830.5473981.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4408120.7237870.5682950.00.01.00.0
0.0305700.8545450.0286500.1967040.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.7614800.1849960.4338820.5320280.2774420.4404440.3239240.2956860.9172070.1008840.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.4241110.6886930.5977850.00.01.00.0
0.0879540.9818180.0939620.0000271.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.7083290.2401250.4828830.4716360.7473120.4963050.3190300.3479470.9361700.1615780.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4516190.6365180.2537630.00.01.00.0
0.1398700.9818180.1504010.0000201.00.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5965920.1808130.3321840.5884020.2510620.1808400.3290170.2958850.3715520.2480301.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4261700.7162370.6262720.00.01.00.0
0.0757801.0000000.0870960.0000111.00.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.7176880.2027490.3652360.5309260.3345840.2584680.2674070.5035720.8971830.5473981.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4408120.7237870.5682950.00.01.00.0
0.0840800.9818180.0881660.0000091.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.6724750.4105510.3436710.7381580.4794450.2240200.2615940.5220340.7725930.2014031.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4210290.6587250.5834630.00.01.00.0
0.0447850.9454550.0508980.0350250.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.6550070.1841970.4878120.4523050.5012530.2240200.2615940.5220340.7725930.2014031.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.4438930.7537550.5826170.00.01.00.0
0.1053660.9818180.1122240.0000181.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5897340.1665980.3677750.5819240.2470480.5080910.3353020.3668190.6701180.2967401.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4261700.7162370.6262720.00.01.00.0
yearpricemileagestock_typemodel_hashed_0model_hashed_1model_hashed_2model_hashed_3model_hashed_4model_hashed_5model_hashed_6model_hashed_7model_hashed_8model_hashed_9model_hashed_10model_hashed_11model_hashed_12model_hashed_13model_hashed_14model_hashed_15model_hashed_16model_hashed_17model_hashed_18model_hashed_19model_hashed_20model_hashed_21model_hashed_22model_hashed_23model_hashed_24model_hashed_25model_hashed_26model_hashed_27model_hashed_28model_hashed_29model_hashed_30model_hashed_31model_hashed_32model_hashed_33model_hashed_34model_hashed_35model_hashed_36model_hashed_37model_hashed_38model_hashed_39model_hashed_40model_hashed_41model_hashed_42model_hashed_43model_hashed_44model_hashed_45model_hashed_46exterior_color_x0exterior_color_x1exterior_color_x2exterior_color_x3exterior_color_x4interior_color_x0interior_color_x1interior_color_x2interior_color_x3interior_color_x4drivetrain_All-wheel Drivedrivetrain_Front-wheel Drivedrivetrain_Rear-wheel Drivedrivetrain_nanmake_Acuramake_Alfa Romeomake_Audimake_BMWmake_Buickmake_Cadillacmake_Chevroletmake_Chryslermake_Dodgemake_Fordmake_GMCmake_Genesismake_Hondamake_Hyundaimake_INFINITImake_Jaguarmake_Jeepmake_Kiamake_Land Rovermake_Lexusmake_Lincolnmake_Mazdamake_Mercedes-Benzmake_Nissanmake_Porschemake_RAMmake_Subarumake_Teslamake_Toyotamake_Volkswagenmake_Volvobodystyle_Cargo Vanbodystyle_Convertiblebodystyle_Coupebodystyle_Hatchbackbodystyle_Minivanbodystyle_Passenger Vanbodystyle_Pickup Truckbodystyle_SUVbodystyle_Sedanbodystyle_Wagonbodystyle_nancat_x0cat_x1cat_x2fuel_type_Electricfuel_type_Flexiblefuel_type_Gasolinefuel_type_Hybrid
msrp
0.1605700.9818180.1643240.0000021.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.6542250.3381150.6883570.6034450.8764600.3143900.3492620.4024700.7924450.2453221.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4088580.7886500.4657210.00.01.00.0
0.1465170.9818180.1569670.0000271.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.7176880.2027490.3652360.5309260.3345840.3907700.7821430.5223010.2795020.0312001.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4261700.7162370.6262720.00.01.00.0
0.1099130.9454550.1119240.0747410.00.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.6460490.1603830.6462760.5786700.8064670.2043010.4186530.4319100.8221620.2530101.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.4837760.7499240.0318360.00.01.00.0
0.1221830.9636360.1329300.0000141.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.1591220.6701600.5842691.0000000.4671810.0405200.4791410.5664570.6535000.2327281.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.6079610.0000000.5463000.00.01.00.0
0.0221430.7454550.0341150.1505180.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.7222530.3920700.4841160.8417310.3074350.2240200.2615940.5220340.7725930.2014030.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.4241110.6886930.5977850.00.01.00.0
0.1248750.9272730.1255460.0378190.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.3051240.5318810.1118570.2206220.4376360.2043010.4186530.4319100.8221620.2530101.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.4837760.7499240.0318360.00.01.00.0
0.3044620.9818180.3129780.0000161.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.7193940.3879040.3510780.4811040.5507200.2584680.2674070.5035720.8971830.5473981.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4697220.1850860.5768541.00.00.00.0
0.1322840.9818180.1401780.0000021.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.5469720.1476780.5572500.3493980.5023720.2043010.4186530.4319100.8221620.2530101.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4261700.7162370.6262720.00.01.00.0
0.0592290.9090910.0586860.0380670.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.8000100.1532110.6441690.3861010.3901440.2240200.2615940.5220340.7725930.2014030.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4408120.7237870.5682950.00.01.00.0
0.0203310.7818180.0245690.0741080.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.3985030.3643480.3318400.7470500.6172390.3421300.2592230.4638440.7462580.4713420.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.4438930.7537550.5826170.00.01.00.0

Duplicate rows

Most frequently occurring

yearpricemileagestock_typemodel_hashed_0model_hashed_1model_hashed_2model_hashed_3model_hashed_4model_hashed_5model_hashed_6model_hashed_7model_hashed_8model_hashed_9model_hashed_10model_hashed_11model_hashed_12model_hashed_13model_hashed_14model_hashed_15model_hashed_16model_hashed_17model_hashed_18model_hashed_19model_hashed_20model_hashed_21model_hashed_22model_hashed_23model_hashed_24model_hashed_25model_hashed_26model_hashed_27model_hashed_28model_hashed_29model_hashed_30model_hashed_31model_hashed_32model_hashed_33model_hashed_34model_hashed_35model_hashed_36model_hashed_37model_hashed_38model_hashed_39model_hashed_40model_hashed_41model_hashed_42model_hashed_43model_hashed_44model_hashed_45model_hashed_46exterior_color_x0exterior_color_x1exterior_color_x2exterior_color_x3exterior_color_x4interior_color_x0interior_color_x1interior_color_x2interior_color_x3interior_color_x4drivetrain_All-wheel Drivedrivetrain_Front-wheel Drivedrivetrain_Rear-wheel Drivedrivetrain_nanmake_Acuramake_Alfa Romeomake_Audimake_BMWmake_Buickmake_Cadillacmake_Chevroletmake_Chryslermake_Dodgemake_Fordmake_GMCmake_Genesismake_Hondamake_Hyundaimake_INFINITImake_Jaguarmake_Jeepmake_Kiamake_Land Rovermake_Lexusmake_Lincolnmake_Mazdamake_Mercedes-Benzmake_Nissanmake_Porschemake_RAMmake_Subarumake_Teslamake_Toyotamake_Volkswagenmake_Volvobodystyle_Cargo Vanbodystyle_Convertiblebodystyle_Coupebodystyle_Hatchbackbodystyle_Minivanbodystyle_Passenger Vanbodystyle_Pickup Truckbodystyle_SUVbodystyle_Sedanbodystyle_Wagonbodystyle_nancat_x0cat_x1cat_x2fuel_type_Electricfuel_type_Flexiblefuel_type_Gasolinefuel_type_Hybrid# duplicates
1960.9818180.0588690.0000231.00.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.6680080.1750140.3366340.5426000.2063160.2668470.3419130.4165070.8021520.2425310.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.4438930.7537550.5826170.00.01.00.011
15660.9818180.1928640.0000001.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.3166110.2909930.1929340.5559210.2436350.3313520.3044530.4328660.8647310.3998531.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.4837760.7499240.0318360.00.01.00.010
6920.9818180.0832740.0000231.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.3443880.4495570.3931480.6831020.7282890.4526890.3544230.3709870.7380930.2193331.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4408120.7237870.5682950.00.01.00.09
15670.9818180.1935470.0000001.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.51.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.3166110.2909930.1929340.5559210.2436350.3313520.3044530.4328660.8647310.3998531.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.4837760.7499240.0318360.00.01.00.09
3910.9818180.0695570.0000071.00.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.00.50.50.50.50.50.50.50.5550500.1409530.3800840.5342020.1733480.2043010.4186530.4319100.8221620.2530100.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.4408120.7237870.5682950.00.01.00.08
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